Human gut microbiota produce a variety of molecules, some of which enter the bloodstream and impact health. Conversely, dietary or pharmacological compounds may affect the microbiota before entering the circulation. Characterization of these interactions is an important step towards understanding the effects of the gut microbiota on health. In this cross-sectional study, we used deep metagenomic sequencing and ultra-high-performance liquid chromatography linked to mass spectrometry for a detailed characterization of the gut microbiota and plasma metabolome, respectively, of 8583 participants invited at age 50 to 64 from the population-based Swedish CArdioPulmonary bioImage Study. Here, we find that the gut microbiota explain up to 46% of the variance of individual plasma metabolites and we present 997 associations between alpha diversity and plasma metabolites and 546,819 associations between specific gut metagenomic species and plasma metabolites in an online atlas (https://gutsyatlas.serve.scilifelab.se/). We exemplify the potential of this resource by presenting novel associations between dietary factors and oral medication with the gut microbiome, and microbial species strongly associated with the uremic toxinp-cresol sulfate. This resource can be used as the basis for targeted studies of perturbation of specific metabolites and for identification of candidate plasma biomarkers of gut microbiota composition.

Context Emerging evidence has related the gut microbiome and circulating metabolites to human obesity. Gut microbiota is responsible for several metabolic functions, and altered plasma metabolome might reflect differences in the gut microbiome. Objective To identify a plasma metabolite profile associated with body mass index (BMI) in a general population and investigate whether such metabolite profile is associated with distinct composition of the gut microbiota. Design Targeted profiling of 48 plasma metabolites was performed in a population of 920 Swedish adults (mean age, 39 years; 53% women) from the ongoing Malmö Offspring Study using targeted liquid chromatography–mass spectrometry. Gut microbiota was analyzed by sequencing the 16S ribosomal RNA gene (V1-V3 region) in fecal samples of 674 study participants. Results BMI was associated with 19 metabolites (P < 0.001 for all), of which glutamate provided the strongest direct association (P = 5.2e-53). By orthogonal partial least squares regression, a metabolite principal component predictive of BMI was constructed (PC_BMI). In addition to glutamate, PC_BMI was dominated by branched-chain amino acids (BCAAs) and related metabolites. Four gut microbiota genera (Blautia, Dorea, Ruminococcus, and SHA-98) were associated with both BMI and PC_BMI (P < 8.0e-4 for all). When simultaneously regressing PC_BMI and metabolite-associated gut bacteria against BMI, only PC_BMI remained statistically significant. Conclusions We discovered associations between four gut microbiota genera (Blautia, Dorea, Ruminococcus, and SHA-98) and BMI-predictive plasma metabolites, including glutamate and BCAAs. Thus, these metabolites could be mediators between gut microbiota and obesity, pointing to potential future opportunities for targeting the gut microbiota in prevention of obesity.

Objectives We recently identified a health conscious food pattern (HCFP) associated with reduced risk of cardiometabolic disease. However, the molecular events linking the healthy food pattern to reduced risk of cardiometabolic disease are unknown. Our aim was to identify plasma metabolites associated with the HCFP and test if such metabolites predict cardiometabolic disease and mortality. Methods Using liquid-chromatography mass-spectrometry, 112 plasma metabolites were measured in 3236 participants without cardiovascular disease (CVD) and diabetes mellitus from the population-based Malmö Diet and Cancer study. Metabolites associated with the HCFP were identified using multivariable adjusted linear regressions followed by Bonferroni correction. The healthy dietary biomarkers were subsequently related to risk of cardiometabolic disease and mortality during long-term follow-up with multivariable adjusted Cox proportional hazards models. Results During a median follow-up time of 21.4 years, 603 participants developed CVD, 362 developed diabetes mellitus and 843 participants died. Five healthy dietary biomarkers were associated with the HCFP at baseline (p<0.0004) and four predicted at least one of the studied end points (p<0.05). Ergothioneine was the metabolite most strongly connected to the HCFP and was associated with a lower risk of coronary disease (HR per 1 SD increment of ergothioneine, HR=0.85, p=0.01), cardiovascular mortality (HR=0.79, p=0.002) and overall mortality (HR=0.86, p=4e-5). Conclusions We identified that higher ergothioneine was an independent marker of lower risk of cardiometabolic disease and mortality, which potentially can be induced by a specific healthy dietary intake.

BACKGROUND: Gut microbiota have been implicated in atherosclerotic disease, but their relation with subclinical coronary atherosclerosis is unclear. This study aimed to identify associations between the gut microbiome and computed tomography–based measures of coronary atherosclerosis and to explore relevant clinical correlates. METHODS: We conducted a cross-sectional study of 8973 participants (50 to 65 years of age) without overt atherosclerotic disease from the population-based SCAPIS (Swedish Cardiopulmonary Bioimage Study). Coronary atherosclerosis was measured using coronary artery calcium score and coronary computed tomography angiography. Gut microbiota species abundance and functional potential were assessed with shotgun metagenomics sequencing of fecal samples, and associations with coronary atherosclerosis were evaluated with multivariable regression models adjusted for cardiovascular risk factors. Associated species were evaluated for association with inflammatory markers, metabolites, and corresponding species in saliva. RESULTS: The mean age of the study sample was 57.4 years, and 53.7% were female. Coronary artery calcification was detected in 40.3%, and 5.4% had at least 1 stenosis with >50% occlusion. Sixty-four species were associated with coronary artery calcium score independent of cardiovascular risk factors, with the strongest associations observed for Streptococcus anginosus and Streptococcus oralis subsp oralis ( P <1×10 ^–5 ). Associations were largely similar across coronary computed tomography angiography–based measurements. Out of the 64 species, 19 species, including streptococci and other species commonly found in the oral cavity, were associated with high-sensitivity C-reactive protein plasma concentrations, and 16 with neutrophil counts. Gut microbial species that are commonly found in the oral cavity were negatively associated with plasma indole propionate and positively associated with plasma secondary bile acids and imidazole propionate. Five species, including 3 streptococci, correlated with the same species in saliva and were associated with worse dental health in the Malmö Offspring Dental Study. Microbial functional potential of dissimilatory nitrate reduction, anaerobic fatty acid β-oxidation, and amino acid degradation were associated with coronary artery calcium score. CONCLUSIONS: This study provides evidence of an association of a gut microbiota composition characterized by increased abundance of Streptococcus spp and other species commonly found in the oral cavity with coronary atherosclerosis and systemic inflammation markers. Further longitudinal and experimental studies are warranted to explore the potential implications of a bacterial component in atherogenesis.

Context Type 2 diabetes mellitus (T2DM) is accompanied by an increased risk for coronary artery disease (CAD), but the overlapping metabolic disturbances preceding both diseases are insufficiently described. Objective We hypothesized that alterations in metabolism occur years before clinical manifestation of T2DM and CAD and that these alterations are reflected in the plasma metabolome. We thus aimed to identify plasma metabolites that predict future T2DM and CAD. Design Through use of targeted liquid chromatography-mass spectrometry, 35 plasma metabolites (amino acid metabolites and acylcarnitines) were quantified in 1049 individuals without CAD and diabetes, drawn from a population sample of 5386 in the Malmö Preventive Project (mean age, 69.5 years; 31% women). The sample included 204 individuals who developed T2DM, 384 who developed CAD, and 496 who remained T2DM and CAD free during a mean follow-up of 6.1 years. Results In total, 16 metabolites were significantly associated with risk for developing T2DM according to logistic regression models. Glutamate (OR, 1.96; P = 5.4e-12) was the most strongly associated metabolite, followed by increased levels of branched-chain amino acids. Incident CAD was predicted by three metabolites: glutamate (OR, 1.28; P = 6.6e-4), histidine (OR, 0.76; P = 5.1e-4), and asparagine (OR, 0.80; P = 2.2e-3). Glutamate (OR, 1.48; P = 1.6e-8) and asparagine (OR, 0.75; P = 1.8e-5) were both associated with a composite endpoint of developing T2DM or CAD. Conclusion Several plasma metabolites were associated with incidence of T2DM and CAD; elevated glutamate and reduced asparagine levels were associated with both diseases. We thus discovered associations that might help shed additional light on why T2DM and CAD commonly co-occur.